In order to ensure that digital data complies with business, security and other policies, the trend in recent years has been to subject such data to an ever increasing number of pre-access evaluation processes. Examples of such processes include hygiene scans, filtering, classifications, and data analysis. Particularly computationally intensive operations may include, for example, virus/spyware scans, spam detection, keyword detections, malicious/inappropriate/prohibited URL detection, data leakage prevention, data classification, etc.
The number of scanning/classification technologies that a piece of content needs be subjected to has continued to increase over time. In addition, the size of a typical piece of content that needs to be scanned has trended upwards and has shown no sign of leveling off. Both of these trends result in an ever increasing amount of computer resources (CPU, memory, network bandwidth, etc.) that are needed to perform scanning/classification.
The problem is further exacerbated by the fact that the data generally needs to be repeatedly re-analyzed, rescanned, and/or reclassified by various security and compliance products as it moves within or across computers networks. These products are typically installed on desktops, notebooks, different servers (like mail, file, collaboration, etc.), and services in the cloud. As data traverses each of these way points, the same computationally intensive operations are often performed over and over again. This leads to decreased performance and throughput of the system and requires installation of additional hardware, software, etc. In the case of services, the additional overhead can have a direct impact on the profitability of the service.